Quantum associative memory with distributed queries

Ezhov, Alexandr A.; Nifanova, A. V.; Ventura, Dan

doi:10.1016/s0020-0255(00)00057-8

Cited by 48 publications

(20 citation statements)

References 18 publications

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“…The latter seems to be needed (e.g., Bob & Faber, 1999) and promising (e.g., Dubois, 2000a,b;Pessa & Vitiello, 1999) for modeling quantum background of conscious processing (cf., Ezhov et al, 2000Ezhov et al, , 2001Weinacht et al, 1999;Rabitz et al, NeuroQuantology 2003;2: 200- Snider et al, 1999;Spencer, 2001;Wheeler & Zurek, 1983;Jones et al, 2000 -for hints from frontier technology). According to the holonomic theory (Pribram, 1991), holography-like parallel-distributed processing in dendritic networks of V1 is essential for image processing.…”

Section: Discussionmentioning

confidence: 99%

Conscious Image Processing: An Integrated Neural and Quantum Model

Peruš¹

2007

Neuroquantology

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An integrated model of conscious image processing in human cortex is proposed based on the Holonomic Brain Theory by Karl Pribram and related models. After optimal delineation of the neural and quantum ingredients, the model combines the predominantly (sub)neuronal image processing and the essentially quantum-based repetitive act of becoming-conscious of the resulting phenomenal image. The model optimally incorporates contemporary limited knowledge starting from a systematic search for fit between existing computational models, and between available experimental data, and between data and models. Since we are not yet able to tackle qualitative conscious experience directly, processes for making an image (or result of image processing, respectively) conscious are discussed.A quantum implementation of holography-like processing of images in the striate cortex (V1) is p roposed using a computational model called quantum associative network. The input to the quantum net could be the Gabor wavelets, together with their coefficients, which are infomax-constrained spectral and sparse neural codes produced in the convolutional cascade along the retino-geniculo-striate visual pathway using the receptive fields as determined by dendritic processes. Perceptual projections are used as argument for holography-like and quantum essence of visual phenomena, because classically (neurally) alone they could not be produced in such a quality. Level-invariant image attractors are argued to be representations to become conscious in/by a subject, after a similar stimulus has triggered the wave-function collapse (i.e., recall from memory). Auxiliary representations for simultaneous subconscious processing, based on phase-information, for associative vision-based cognition are proposed to be Gabor wavelets (i.e., spectral codes in V1 receptive fields, or dendritic trees, respectively) and their c oefficients (i.e., sparse codes in activities of V1 neurons).

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Section: Discussionmentioning

confidence: 99%

Conscious Image Processing: An Integrated Neural and Quantum Model

Peruš¹

2007

Neuroquantology

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“…This method that uses two operators of formula (4) and formula (5) and adopts special encoding strategy of original state can recognize multi-pattern with the probability of 100%. For this case, idiographic method is as follow:…”

Section: Searching Methods With the Probability Of 100%mentioning

confidence: 99%

“…For example, in 2000, Ezhov and Ventura proposed a model of quantum associative memory with distributed queries [5,6]. In 2001, it was also Ezhov who realized pattern recognition using quantum entanglement and Everett interpretation of quantum mechanics [7].…”

Section: Introductionmentioning

confidence: 99%

Quantum Pattern Recognition with Probability of 100%

Zhou

Qiu-lin

2007

Int J Theor Phys

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In recent years there has been an increasing focus on the quantum pattern recognition, especially quantum multi-pattern recognition in computer science. This paper presents a new quantum multi-pattern recognition method based on the improved Grover's algorithm. This method not only details the process of quantum multi-pattern recognition using several unitary operators, but also introduces a new design scheme of initializing quantum state and quantum encoding on the pattern set. If the rate of the number of the recognized pattern on the total patterns is over 1/3, this new method can recognize multi-pattern simultaneously with the probability of 100%. Mathematic calculations and simulation results on the case show that the proposed method can accomplish multi-pattern recognition with the probability of 100%. However, the recognition probability of other pattern recognition methods is impossible to reach 1.

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“…This can be accomplished with further modification of the basic quantum memory model we have been discussing. This modification involves the use of distributed queries and is presented in detail in [37]. Briefly, a distributed query is a distribution of the form…”

Section: Recall -Associationmentioning

confidence: 99%

“…The index p marks one of these states, p , which is the center of the distribution (real-valued amplitudes are distributed such that the maximal value occurs at this center, and the amplitudes of the other basis states decrease monotonically with Hamming distance from the center state). This leads to the introduction of spurious memories into the recall process; however, counter to intuition the presence of these spurious memories may actually facilitate memory recall [37]. Table 3 summarizes the analogies used in developing a quantum associative memory.…”

Section: Recall -Associationmentioning

confidence: 99%

Quantum Neural Networks

Ezhov

Ventura

2000

Studies in Fuzziness and Soft Computing

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101

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Abstract. This chapter outlines the research, development and perspectives of quantum neural networks -a burgeoning new field which integrates classical neurocomputing with quantum computation [1]. It is argued that the study of quantum neural networks may give us both new undestanding of brain function as well as unprecedented possibilities in creating new systems for information processing, including solving classically intractable problems, associative memory with exponential capacity and possibly overcoming the limitations posed by the Church-Turing thesis. Keywords. Quantum neural networks, associative memory, entanglement, many universes interpretation Why quantum neural networks?There are two main reasons to discuss quantum neural networks. One has its origin in arguments for the essential role which quantum processes play in the living brain. For example, Roger Penrose has argued that a new physics binding quantum phenomena with general relativity can explain such mental abilities as understanding, awareness and consciousness [2]. However, this approach advocates the study of intracellular structures, such as microtubules rather than that of the networks of neurons themselves [3]. A second motivation is the possibility that the field of classical artificial neural networks can be generalized to the quantum domain by eclectic combination of that field with the promising new field of quantum computing [4]. Both considerations suggest new understanding of mind and brain function as well as new unprecedented abilities in information processing. Here we consider quantum neural networks as the next natural step in the evolution of neurocomputing systems, focusing our attention on artificial rather than biological systems. We outline different approaches to the

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Quantum associative memory with distributed queries

Cited by 48 publications

References 18 publications

Conscious Image Processing: An Integrated Neural and Quantum Model

Conscious Image Processing: An Integrated Neural and Quantum Model

Quantum Pattern Recognition with Probability of 100%

Quantum Neural Networks

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